Abstract
Abstract. Critical data selection is essential for determining representative baseline levels of atmospheric trace gases even at remote measurement sites. Different data selection techniques have been used around the world, which could potentially lead to reduced compatibility when comparing data from different stations. This paper presents a novel statistical data selection method named adaptive diurnal minimum variation selection (ADVS) based on CO2 diurnal patterns typically occurring at elevated mountain stations. Its capability and applicability were studied on records of atmospheric CO2 observations at six Global Atmosphere Watch stations in Europe, namely, Zugspitze-Schneefernerhaus (Germany), Sonnblick (Austria), Jungfraujoch (Switzerland), Izaña (Spain), Schauinsland (Germany), and Hohenpeissenberg (Germany). Three other frequently applied statistical data selection methods were included for comparison. Among the studied methods, our ADVS method resulted in a lower fraction of data selected as a baseline with lower maxima during winter and higher minima during summer in the selected data. The measured time series were analyzed for long-term trends and seasonality by a seasonal-trend decomposition technique. In contrast to unselected data, mean annual growth rates of all selected datasets were not significantly different among the sites, except for the data recorded at Schauinsland. However, clear differences were found in the annual amplitudes as well as the seasonal time structure. Based on a pairwise analysis of correlations between stations on the seasonal-trend decomposed components by statistical data selection, we conclude that the baseline identified by the ADVS method is a better representation of lower free tropospheric (LFT) conditions than baselines identified by the other methods.
Highlights
Continuous in situ measurements of greenhouse gases (GHGs) at remote locations have been established since 1958 (Keeling, 1960)
The present study focuses on the comparison of results from previous statistical data selection methods with the new adaptive diurnal minimum variation selection (ADVS) method proposed in this study
We presented the novel statistical ADVS method for selecting representative baseline data for CO2 measurements at elevated Global Atmosphere Watch (GAW) mountain stations
Summary
Continuous in situ measurements of greenhouse gases (GHGs) at remote locations have been established since 1958 (Keeling, 1960). A critical issue when using data from remote stations remains the identification of time periods that are representative of larger spatial areas and their differentiation from periods influenced by local and regional pollution. If these two regimes are well disaggregated, the available datasets can represent more reliable information about long-term changes of undisturbed atmospheric GHG levels or be used to investigate local and regional GHG sources and sinks when analyzing deviations from baseline conditions. Baseline data selection starts with validated data and identifies in subsequent steps a final subset of the validated dataset based on predefined criteria for specific qualities such as representativeness. These data will be referred to as “selected baseline data” or as “selected data” in the following
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